Performance assessment of real-time estimation of continuous-time stochastic volatility of financial data on GPUs
Article No.: 9, Pages 1 - 4
Abstract
Real-time Bayes estimation of stochastic volatility for financial ultra-high frequency data is plagued with the curse of high dimensionality. Methods have been developed to manage this problem through the use a parallel computing, relying on supercomputing and GPU resources. In the technical paper, we endeavor to assess the performance of GPU computing and show that an adequately equipped GPU workstation can rise to the task, producing reasonably real-time results with actual data from financial markets.
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Index Terms
- Performance assessment of real-time estimation of continuous-time stochastic volatility of financial data on GPUs
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- San Diego Super Computing Ctr: San Diego Super Computing Ctr
- HPCWire: HPCWire
- Omnibond: Omnibond Systems, LLC
- SGI
- Internet2
- Indiana University: Indiana University
- CASC: The Coalition for Academic Scientific Computation
- NICS: National Institute for Computational Sciences
- Intel: Intel
- DDN: DataDirect Networks, Inc
- DELL
- CORSA: CORSA Technology
- ALLINEA: Allinea Software
- Cray
- RENCI: Renaissance Computing Institute
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 July 2015
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- Short-paper
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XSEDE '15
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- San Diego Super Computing Ctr
- HPCWire
- Omnibond
- Indiana University
- CASC
- NICS
- Intel
- DDN
- CORSA
- ALLINEA
- RENCI
XSEDE '15: Extreme Science Engineering Discovery Environment 2015 Conference
July 26 - 30, 2015
Missouri, St. Louis
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XSEDE '15 Paper Acceptance Rate 49 of 70 submissions, 70%;
Overall Acceptance Rate 129 of 190 submissions, 68%
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